Overview

Dataset statistics

Number of variables15
Number of observations17908
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory120.0 B

Variable types

Numeric11
Categorical4

Alerts

avg_risk_score has unique valuesUnique
years_employed has 694 (3.9%) zerosZeros
current_address_year has 1486 (8.3%) zerosZeros

Reproduction

Analysis started2023-11-08 04:27:50.106514
Analysis finished2023-11-08 04:28:09.697939
Duration19.59 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

entry_id
Real number (ℝ)

Distinct17888
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5596977.6
Minimum1111398
Maximum9999874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:09.848803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1111398
5-th percentile1571268.4
Q13378998.8
median5608376
Q37805624.2
95-th percentile9563219.1
Maximum9999874
Range8888476
Interquartile range (IQR)4426625.5

Descriptive statistics

Standard deviation2562472.8
Coefficient of variation (CV)0.45783152
Kurtosis-1.2008191
Mean5596977.6
Median Absolute Deviation (MAD)2214168.5
Skewness-0.015730158
Sum1.0023068 × 1011
Variance6.5662666 × 1012
MonotonicityNot monotonic
2023-11-07T23:28:10.055270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6848675 2
 
< 0.1%
5946902 2
 
< 0.1%
4455352 2
 
< 0.1%
6561306 2
 
< 0.1%
5896278 2
 
< 0.1%
6739041 2
 
< 0.1%
8156839 2
 
< 0.1%
3903643 2
 
< 0.1%
5659209 2
 
< 0.1%
4235576 2
 
< 0.1%
Other values (17878) 17888
99.9%
ValueCountFrequency (%)
1111398 1
< 0.1%
1111512 1
< 0.1%
1111600 1
< 0.1%
1112315 1
< 0.1%
1112537 1
< 0.1%
1112907 1
< 0.1%
1114070 1
< 0.1%
1114089 1
< 0.1%
1114268 1
< 0.1%
1114275 1
< 0.1%
ValueCountFrequency (%)
9999874 1
< 0.1%
9999421 1
< 0.1%
9998678 1
< 0.1%
9997871 1
< 0.1%
9997796 1
< 0.1%
9997128 1
< 0.1%
9997079 1
< 0.1%
9995957 1
< 0.1%
9995338 1
< 0.1%
9995118 1
< 0.1%

age
Real number (ℝ)

Distinct72
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.015412
Minimum18
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:10.237040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q134
median42
Q351
95-th percentile63
Maximum96
Range78
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.873107
Coefficient of variation (CV)0.27601983
Kurtosis-0.36032326
Mean43.015412
Median Absolute Deviation (MAD)9
Skewness0.31648274
Sum770320
Variance140.97067
MonotonicityNot monotonic
2023-11-07T23:28:10.418304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 570
 
3.2%
43 559
 
3.1%
38 547
 
3.1%
39 546
 
3.0%
44 543
 
3.0%
34 531
 
3.0%
42 522
 
2.9%
40 520
 
2.9%
45 515
 
2.9%
46 513
 
2.9%
Other values (62) 12542
70.0%
ValueCountFrequency (%)
18 31
 
0.2%
19 47
 
0.3%
20 73
 
0.4%
21 77
 
0.4%
22 143
0.8%
23 185
1.0%
24 225
1.3%
25 241
1.3%
26 291
1.6%
27 335
1.9%
ValueCountFrequency (%)
96 1
 
< 0.1%
89 1
 
< 0.1%
87 1
 
< 0.1%
86 3
 
< 0.1%
85 3
 
< 0.1%
84 6
< 0.1%
83 2
 
< 0.1%
82 4
 
< 0.1%
81 3
 
< 0.1%
80 14
0.1%

monthly_income
Real number (ℝ)

Distinct2284
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3657.2147
Minimum905
Maximum9985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:10.602275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum905
5-th percentile1705
Q12580
median3260
Q34670
95-th percentile6585
Maximum9985
Range9080
Interquartile range (IQR)2090

Descriptive statistics

Standard deviation1504.8901
Coefficient of variation (CV)0.4114853
Kurtosis0.86031309
Mean3657.2147
Median Absolute Deviation (MAD)917.5
Skewness0.97023786
Sum65493400
Variance2264694.1
MonotonicityNot monotonic
2023-11-07T23:28:10.778270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3270 62
 
0.3%
3255 61
 
0.3%
3055 60
 
0.3%
3210 58
 
0.3%
3265 58
 
0.3%
3145 57
 
0.3%
3090 56
 
0.3%
3165 56
 
0.3%
3260 55
 
0.3%
3135 54
 
0.3%
Other values (2274) 17331
96.8%
ValueCountFrequency (%)
905 1
 
< 0.1%
1015 2
< 0.1%
1030 1
 
< 0.1%
1055 3
< 0.1%
1090 1
 
< 0.1%
1095 1
 
< 0.1%
1130 1
 
< 0.1%
1140 1
 
< 0.1%
1145 1
 
< 0.1%
1150 1
 
< 0.1%
ValueCountFrequency (%)
9985 1
< 0.1%
9970 1
< 0.1%
9925 1
< 0.1%
9915 1
< 0.1%
9885 1
< 0.1%
9870 1
< 0.1%
9834 1
< 0.1%
9813 1
< 0.1%
9755 1
< 0.1%
9660 1
< 0.1%

years_employed
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5268595
Minimum0
Maximum16
Zeros694
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:10.919535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2597317
Coefficient of variation (CV)0.64072065
Kurtosis0.87386687
Mean3.5268595
Median Absolute Deviation (MAD)1
Skewness0.91271748
Sum63159
Variance5.1063875
MonotonicityNot monotonic
2023-11-07T23:28:11.068644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 3856
21.5%
3 3526
19.7%
1 2420
13.5%
6 1990
11.1%
5 1956
10.9%
4 1919
10.7%
0 694
 
3.9%
7 589
 
3.3%
9 376
 
2.1%
10 287
 
1.6%
Other values (7) 295
 
1.6%
ValueCountFrequency (%)
0 694
 
3.9%
1 2420
13.5%
2 3856
21.5%
3 3526
19.7%
4 1919
10.7%
5 1956
10.9%
6 1990
11.1%
7 589
 
3.3%
8 216
 
1.2%
9 376
 
2.1%
ValueCountFrequency (%)
16 2
 
< 0.1%
15 5
 
< 0.1%
14 6
 
< 0.1%
13 9
 
0.1%
12 13
 
0.1%
11 44
 
0.2%
10 287
1.6%
9 376
2.1%
8 216
 
1.2%
7 589
3.3%

current_address_year
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5847107
Minimum0
Maximum12
Zeros1486
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:11.236333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile9
Maximum12
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7519367
Coefficient of variation (CV)0.76768723
Kurtosis0.040406004
Mean3.5847107
Median Absolute Deviation (MAD)2
Skewness0.90347597
Sum64195
Variance7.5731554
MonotonicityNot monotonic
2023-11-07T23:28:11.397574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 3628
20.3%
1 2772
15.5%
3 2762
15.4%
4 1994
11.1%
0 1486
8.3%
5 1286
 
7.2%
6 1182
 
6.6%
9 692
 
3.9%
8 652
 
3.6%
7 617
 
3.4%
Other values (3) 837
 
4.7%
ValueCountFrequency (%)
0 1486
8.3%
1 2772
15.5%
2 3628
20.3%
3 2762
15.4%
4 1994
11.1%
5 1286
 
7.2%
6 1182
 
6.6%
7 617
 
3.4%
8 652
 
3.6%
9 692
 
3.9%
ValueCountFrequency (%)
12 11
 
0.1%
11 228
 
1.3%
10 598
 
3.3%
9 692
 
3.9%
8 652
 
3.6%
7 617
 
3.4%
6 1182
6.6%
5 1286
7.2%
4 1994
11.1%
3 2762
15.4%

amount_requested
Real number (ℝ)

Distinct98
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean950.44645
Minimum350
Maximum10200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:11.581776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile400
Q1600
median700
Q31100
95-th percentile2600
Maximum10200
Range9850
Interquartile range (IQR)500

Descriptive statistics

Standard deviation698.54368
Coefficient of variation (CV)0.73496375
Kurtosis24.574112
Mean950.44645
Median Absolute Deviation (MAD)200
Skewness3.5995791
Sum17020595
Variance487963.28
MonotonicityNot monotonic
2023-11-07T23:28:11.769164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 2503
14.0%
700 2317
12.9%
400 2252
12.6%
800 1715
9.6%
900 1466
8.2%
500 1357
 
7.6%
1100 1094
 
6.1%
1200 896
 
5.0%
1000 477
 
2.7%
550 244
 
1.4%
Other values (88) 3587
20.0%
ValueCountFrequency (%)
350 46
 
0.3%
375 1
 
< 0.1%
400 2252
12.6%
401 13
 
0.1%
425 10
 
0.1%
450 224
 
1.3%
475 3
 
< 0.1%
500 1357
7.6%
501 14
 
0.1%
525 8
 
< 0.1%
ValueCountFrequency (%)
10200 1
 
< 0.1%
10100 4
 
< 0.1%
9900 3
 
< 0.1%
9800 3
 
< 0.1%
8300 1
 
< 0.1%
7900 1
 
< 0.1%
7800 1
 
< 0.1%
6300 1
 
< 0.1%
5800 1
 
< 0.1%
5200 20
0.1%

risk_score
Real number (ℝ)

Distinct1411
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61086.302
Minimum2100
Maximum99750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:11.948545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2100
5-th percentile35450
Q149350
median61200
Q372750
95-th percentile85700
Maximum99750
Range97650
Interquartile range (IQR)23400

Descriptive statistics

Standard deviation15394.255
Coefficient of variation (CV)0.2520083
Kurtosis-0.67746693
Mean61086.302
Median Absolute Deviation (MAD)11700
Skewness-0.022398347
Sum1.0939335 × 109
Variance2.3698309 × 108
MonotonicityNot monotonic
2023-11-07T23:28:12.137467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60300 41
 
0.2%
52800 38
 
0.2%
62850 38
 
0.2%
68100 37
 
0.2%
72450 37
 
0.2%
72750 37
 
0.2%
49350 35
 
0.2%
38100 35
 
0.2%
60000 34
 
0.2%
67950 34
 
0.2%
Other values (1401) 17542
98.0%
ValueCountFrequency (%)
2100 1
< 0.1%
2250 1
< 0.1%
2800 1
< 0.1%
4450 1
< 0.1%
6100 1
< 0.1%
11100 1
< 0.1%
11850 1
< 0.1%
13750 1
< 0.1%
15500 1
< 0.1%
15850 1
< 0.1%
ValueCountFrequency (%)
99750 1
< 0.1%
99600 1
< 0.1%
99550 1
< 0.1%
99450 1
< 0.1%
99300 1
< 0.1%
99200 1
< 0.1%
99150 1
< 0.1%
99000 1
< 0.1%
98950 1
< 0.1%
98900 1
< 0.1%

inquiries_last_month
Real number (ℝ)

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4572258
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:12.293328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile13
Maximum30
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6730925
Coefficient of variation (CV)0.56883445
Kurtosis5.6711798
Mean6.4572258
Median Absolute Deviation (MAD)2
Skewness1.916539
Sum115636
Variance13.491609
MonotonicityNot monotonic
2023-11-07T23:28:12.658839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6 2918
16.3%
5 2798
15.6%
4 2351
13.1%
3 1910
10.7%
7 1796
10.0%
8 1330
7.4%
2 1254
7.0%
9 892
 
5.0%
10 672
 
3.8%
11 497
 
2.8%
Other values (20) 1490
8.3%
ValueCountFrequency (%)
1 7
 
< 0.1%
2 1254
7.0%
3 1910
10.7%
4 2351
13.1%
5 2798
15.6%
6 2918
16.3%
7 1796
10.0%
8 1330
7.4%
9 892
 
5.0%
10 672
 
3.8%
ValueCountFrequency (%)
30 4
 
< 0.1%
29 5
 
< 0.1%
28 11
 
0.1%
27 17
 
0.1%
26 16
 
0.1%
25 11
 
0.1%
24 20
0.1%
23 17
 
0.1%
22 32
0.2%
21 44
0.2%

pay_schedule
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.0 KiB
bi-weekly
10716 
weekly
3696 
semi-monthly
2004 
monthly
1492 

Length

Max length12
Median length9
Mean length8.5499218
Min length6

Characters and Unicode

Total characters153112
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbi-weekly
2nd rowweekly
3rd rowweekly
4th rowbi-weekly
5th rowsemi-monthly

Common Values

ValueCountFrequency (%)
bi-weekly 10716
59.8%
weekly 3696
 
20.6%
semi-monthly 2004
 
11.2%
monthly 1492
 
8.3%

Length

2023-11-07T23:28:12.823791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T23:28:12.981626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
bi-weekly 10716
59.8%
weekly 3696
 
20.6%
semi-monthly 2004
 
11.2%
monthly 1492
 
8.3%

Most occurring characters

ValueCountFrequency (%)
e 30828
20.1%
l 17908
11.7%
y 17908
11.7%
w 14412
9.4%
k 14412
9.4%
i 12720
8.3%
- 12720
8.3%
b 10716
 
7.0%
m 5500
 
3.6%
o 3496
 
2.3%
Other values (4) 12492
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 140392
91.7%
Dash Punctuation 12720
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30828
22.0%
l 17908
12.8%
y 17908
12.8%
w 14412
10.3%
k 14412
10.3%
i 12720
9.1%
b 10716
 
7.6%
m 5500
 
3.9%
o 3496
 
2.5%
n 3496
 
2.5%
Other values (3) 8996
 
6.4%
Dash Punctuation
ValueCountFrequency (%)
- 12720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 140392
91.7%
Common 12720
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30828
22.0%
l 17908
12.8%
y 17908
12.8%
w 14412
10.3%
k 14412
10.3%
i 12720
9.1%
b 10716
 
7.6%
m 5500
 
3.9%
o 3496
 
2.5%
n 3496
 
2.5%
Other values (3) 8996
 
6.4%
Common
ValueCountFrequency (%)
- 12720
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30828
20.1%
l 17908
11.7%
y 17908
11.7%
w 14412
9.4%
k 14412
9.4%
i 12720
8.3%
- 12720
8.3%
b 10716
 
7.0%
m 5500
 
3.6%
o 3496
 
2.3%
Other values (4) 12492
8.2%

home_owner
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.0 KiB
0
10294 
1
7614 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

Length

2023-11-07T23:28:13.137111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T23:28:13.268987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

Most occurring characters

ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

Most occurring scripts

ValueCountFrequency (%)
Common 17908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

has_debt
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.0 KiB
1
14244 
0
3664 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

Length

2023-11-07T23:28:13.407333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T23:28:13.533378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

Most occurring characters

ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

Most occurring scripts

ValueCountFrequency (%)
Common 17908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

personal_account_months
Real number (ℝ)

Distinct131
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.467389
Minimum0
Maximum183
Zeros130
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:13.680805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q129
median40
Q354
95-th percentile90
Maximum183
Range183
Interquartile range (IQR)25

Descriptive statistics

Standard deviation23.258829
Coefficient of variation (CV)0.5115497
Kurtosis1.8929996
Mean45.467389
Median Absolute Deviation (MAD)11
Skewness1.1358997
Sum814230
Variance540.97314
MonotonicityNot monotonic
2023-11-07T23:28:13.865285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 1715
 
9.6%
50 1478
 
8.3%
42 939
 
5.2%
37 845
 
4.7%
26 752
 
4.2%
54 711
 
4.0%
28 668
 
3.7%
49 646
 
3.6%
27 644
 
3.6%
29 633
 
3.5%
Other values (121) 8877
49.6%
ValueCountFrequency (%)
0 130
0.7%
1 138
0.8%
2 130
0.7%
12 133
0.7%
13 128
0.7%
14 175
1.0%
15 180
1.0%
16 167
0.9%
17 143
0.8%
18 167
0.9%
ValueCountFrequency (%)
183 1
 
< 0.1%
177 1
 
< 0.1%
174 1
 
< 0.1%
171 3
< 0.1%
170 1
 
< 0.1%
164 1
 
< 0.1%
163 1
 
< 0.1%
160 2
< 0.1%
152 2
< 0.1%
151 1
 
< 0.1%

avg_risk_score
Real number (ℝ)

UNIQUE 

Distinct17908
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71763998
Minimum0.35366144
Maximum0.9252519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:14.043480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.35366144
5-th percentile0.59937167
Q10.66868909
median0.71979319
Q30.76909529
95-th percentile0.82979111
Maximum0.9252519
Range0.57159046
Interquartile range (IQR)0.1004062

Descriptive statistics

Standard deviation0.070536147
Coefficient of variation (CV)0.098289043
Kurtosis-0.23807856
Mean0.71763998
Median Absolute Deviation (MAD)0.050185248
Skewness-0.17650318
Sum12851.497
Variance0.004975348
MonotonicityNot monotonic
2023-11-07T23:28:14.215380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.661151188 1
 
< 0.1%
0.7391708393 1
 
< 0.1%
0.8047837 1
 
< 0.1%
0.6810403307 1
 
< 0.1%
0.721912806 1
 
< 0.1%
0.7412434168 1
 
< 0.1%
0.693068724 1
 
< 0.1%
0.7128511548 1
 
< 0.1%
0.6385804647 1
 
< 0.1%
0.7317937425 1
 
< 0.1%
Other values (17898) 17898
99.9%
ValueCountFrequency (%)
0.3536614403 1
< 0.1%
0.359550784 1
< 0.1%
0.3965603783 1
< 0.1%
0.4421952787 1
< 0.1%
0.4470701412 1
< 0.1%
0.4538168017 1
< 0.1%
0.4577382657 1
< 0.1%
0.4654677472 1
< 0.1%
0.46827771 1
< 0.1%
0.469287172 1
< 0.1%
ValueCountFrequency (%)
0.9252519032 1
< 0.1%
0.9184164848 1
< 0.1%
0.9166059018 1
< 0.1%
0.9147883762 1
< 0.1%
0.9113536712 1
< 0.1%
0.9096834637 1
< 0.1%
0.9085165407 1
< 0.1%
0.9072543082 1
< 0.1%
0.9072136758 1
< 0.1%
0.906546489 1
< 0.1%

avg_ext_quality_score
Real number (ℝ)

Distinct17530
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62259032
Minimum0.022057
Maximum0.966953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T23:28:14.396760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.022057
5-th percentile0.4339191
Q10.5477245
median0.627009
Q30.70268925
95-th percentile0.80433
Maximum0.966953
Range0.944896
Interquartile range (IQR)0.15496475

Descriptive statistics

Standard deviation0.11528366
Coefficient of variation (CV)0.18516777
Kurtosis0.46789529
Mean0.62259032
Median Absolute Deviation (MAD)0.077441
Skewness-0.33046772
Sum11149.348
Variance0.013290323
MonotonicityNot monotonic
2023-11-07T23:28:14.582137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.613834 3
 
< 0.1%
0.530815 3
 
< 0.1%
0.700858 3
 
< 0.1%
0.549308 3
 
< 0.1%
0.636598 3
 
< 0.1%
0.25358 3
 
< 0.1%
0.480815 3
 
< 0.1%
0.750858 3
 
< 0.1%
0.51439 3
 
< 0.1%
0.676599 3
 
< 0.1%
Other values (17520) 17878
99.8%
ValueCountFrequency (%)
0.022057 1
< 0.1%
0.056622 1
< 0.1%
0.060184 1
< 0.1%
0.067339 1
< 0.1%
0.075976 1
< 0.1%
0.095458 1
< 0.1%
0.095564 1
< 0.1%
0.10358 2
< 0.1%
0.105403 1
< 0.1%
0.107534 1
< 0.1%
ValueCountFrequency (%)
0.966953 1
< 0.1%
0.963647 1
< 0.1%
0.949883 1
< 0.1%
0.947836 1
< 0.1%
0.942336 1
< 0.1%
0.934158 1
< 0.1%
0.927269 1
< 0.1%
0.922813 1
< 0.1%
0.922073 1
< 0.1%
0.921789 1
< 0.1%

e_signed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.0 KiB
1
9639 
0
8269 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Length

2023-11-07T23:28:14.747304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T23:28:14.872904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Most occurring characters

ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common 17908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Interactions

2023-11-07T23:28:07.916914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:54.095585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:55.533592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:57.040801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:58.487582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:59.825022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:01.125710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:02.444191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:03.743387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:05.048410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:06.613627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:08.031854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:54.224479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:55.652370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:57.156818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:58.603476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:59.938045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:01.240933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:02.558291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:03.856465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:05.168319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:06.725666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:08.154625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:54.350191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:55.782265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:57.280514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:58.731452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:00.061652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:01.363482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:02.681756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:03.978904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:05.298094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:06.848386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:08.268182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:54.458058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:55.928919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:57.389417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:58.844740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:00.171767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:01.473075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:02.794943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:04.090302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:05.413958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:06.959233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:08.393864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:54.596756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:56.105509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:57.518072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:58.973554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:00.297554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:01.601386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:02.921495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:04.216974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:05.548897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:07.088895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:08.514782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:54.727868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:56.277052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:57.635092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:59.097691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:00.414594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:01.718904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:03.041338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:04.335274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:05.672852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:07.209042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:08.631979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:54.851106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:56.435403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:57.748499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:59.216349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:00.526551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:01.831885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:03.156440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:04.448966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:05.795244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:07.326242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:08.750810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:54.971219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:56.553734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:57.861900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:59.335478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:00.643984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:01.947624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:03.275663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:04.562410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:06.114548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:07.441383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:08.870115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:55.114036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:56.675014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:57.977667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:59.455772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:00.763545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:02.069721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:03.395444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:04.684121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:06.242586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:07.562512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:08.994672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:55.254235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:56.798801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:58.100122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:59.583034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:00.887900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:02.200832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:03.515249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:04.810876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:06.368053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:07.685075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:09.112566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:55.401884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:56.918761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:58.217628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:27:59.707780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:01.006383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:02.325940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:03.628360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:04.930443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:06.490731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T23:28:07.802518image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-07T23:28:14.979013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
entry_idagemonthly_incomeyears_employedcurrent_address_yearamount_requestedrisk_scoreinquiries_last_monthpersonal_account_monthsavg_risk_scoreavg_ext_quality_scorepay_schedulehome_ownerhas_debte_signed
entry_id1.0000.005-0.012-0.0040.000-0.0040.007-0.005-0.0070.0090.0080.0190.0070.0070.014
age0.0051.0000.1870.1880.1450.0880.1550.0310.0390.0920.0550.1110.1440.0660.094
monthly_income-0.0120.1871.0000.1280.0580.2500.1560.0550.0090.0090.0060.0520.1520.0500.065
years_employed-0.0040.1880.1281.0000.3530.0800.0890.0160.158-0.0150.0370.0380.0330.0540.025
current_address_year0.0000.1450.0580.3531.0000.0640.0740.0220.138-0.1070.0250.0170.1910.0230.009
amount_requested-0.0040.0880.2500.0800.0641.0000.328-0.0200.0380.0610.0190.0540.0750.0380.171
risk_score0.0070.1550.1560.0890.0740.3281.000-0.2350.0050.1970.1480.0330.1370.0230.091
inquiries_last_month-0.0050.0310.0550.0160.022-0.020-0.2351.0000.009-0.066-0.0670.0420.0180.0150.035
personal_account_months-0.0070.0390.0090.1580.1380.0380.0050.0091.0000.0330.0310.0230.0780.1820.092
avg_risk_score0.0090.0920.009-0.015-0.1070.0610.197-0.0660.0331.0000.4060.1370.0870.0000.000
avg_ext_quality_score0.0080.0550.0060.0370.0250.0190.148-0.0670.0310.4061.0000.0230.0590.0260.039
pay_schedule0.0190.1110.0520.0380.0170.0540.0330.0420.0230.1370.0231.0000.0460.0890.033
home_owner0.0070.1440.1520.0330.1910.0750.1370.0180.0780.0870.0590.0461.0000.0760.047
has_debt0.0070.0660.0500.0540.0230.0380.0230.0150.1820.0000.0260.0890.0761.0000.038
e_signed0.0140.0940.0650.0250.0090.1710.0910.0350.0920.0000.0390.0330.0470.0381.000

Missing values

2023-11-07T23:28:09.287891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-07T23:28:09.560489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

entry_idagemonthly_incomeyears_employedcurrent_address_yearamount_requestedrisk_scoreinquiries_last_monthpay_schedulehome_ownerhas_debtpersonal_account_monthsavg_risk_scoreavg_ext_quality_scoree_signed
07629673403135335503620010bi-weekly11300.6611510.4809181
1356042861318063600301509weekly01860.7898410.6807200
2693499723154000450345507weekly01190.6917120.5317120
3568281240523061700421508bi-weekly01860.7931790.6925521
453358193335905211005385012semi-monthly01980.6884830.7446340
5849242321230358600748506weekly01860.6490610.5425561
67948313262795448005080014bi-weekly01730.7445220.6341301
74297036435000211100691005bi-weekly01250.6527020.6259051
86493191325260031150640503semi-monthly01490.6522660.4694591
98908605513055611600597505bi-weekly11280.7131930.7586071
entry_idagemonthly_incomeyears_employedcurrent_address_yearamount_requestedrisk_scoreinquiries_last_monthpay_schedulehome_ownerhas_debtpersonal_account_monthsavg_risk_scoreavg_ext_quality_scoree_signed
17898215097639521552600382005bi-weekly11410.6801270.7161750
178996799343373265411200679502bi-weekly01290.7560190.4816651
17900710087231301521450424506weekly00260.7584220.6163991
17901180735544502523500545009bi-weekly01420.7146720.5349130
17902398322954262021600554506bi-weekly01280.7111690.6295570
17903994972831324553700717002monthly01740.7803610.6777050
17904944244246652521800518003bi-weekly01390.7908300.6249180
179059857590462685511200596509weekly01970.8057990.5720450
17906870847142251535400802003bi-weekly01180.6679240.4065681
179071498559292665410600649504weekly11160.6831110.8461631